Rates of Asymptomatic COVID-19 Infection and Associated Factors in Olmsted County, Minnesota, in the Prevaccination Era.

Objective: To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the prevaccination era. Patients and Methods: We screened first responders (n=191) and Olmsted County employees (n=564) for antibodies to SARS-CoV-2 from November 1, 2020 to February 28, 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all polymerase chain reaction (PCR)-confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, abstracted symptom information, estimated rates of asymptomatic infection and examined related factors. Results: Twenty (10.5%; 95% CI, 6.9%-15.6%) first responders and 38 (6.7%; 95% CI, 5.0%-9.1%) county employees had positive antibodies; an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4 of 20 (20%; 95% CI, 3.0%-37.0%) first responders and 10 of 39 (26%; 95% CI, 12.6%-40.0%) county employees were asymptomatic. Of 6020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385; 95% CI, 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age (0-18 years [odds ratio {OR}, 2.3; 95% CI, 1.7-3.1] and >65 years [OR, 1.40; 95% CI, 1.0-2.0] compared with ages 19-44 years), body mass index (overweight [OR, 0.58; 95% CI, 0.44-0.77] or obese [OR, 0.48; 95% CI, 0.57-0.62] compared with normal or underweight) and tests after November 20, 2020 ([OR, 1.35; 95% CI, 1.13-1.71] compared with prior dates). Conclusion: Asymptomatic rates in Olmsted County before COVID-19 vaccine rollout ranged from 6% to 25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.

Rates of Asymptomatic COVID-19 Infection and Associated Factors in Olmsted County, MN in the Pre-Vaccination Era

Objective To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the pre-vaccination era. Patients and Methods We screened first responders (N=191) and Olmsted County employees (N=564) for antibodies to SARS-CoV-2 from November 2020 to February 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all PCR confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, ed symptom information, estimated rates of asymptomatic infection and examined related factors. Results Twenty (10.5%;95%CI: 6.9%-15.6%) first responders and thirty-eight (6.7%;95% CI: 5.0%-9.1%) county employees had positive antibodies;an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4/20, (20%, 95% CI: 3.0%-37.0%) of first responders and 10/39 (26%, 95% CI: 12.6%-40.0%) county employees, were asymptomatic. Of 6,020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385;95% CI: 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age [0-18 years (OR=2.3, 95% CI: 1.7-3.1) and 65+ years (OR=1.40, 95% CI: 1.0-2.0) compared to ages 19-44 years], body-mass-index [overweight OR=0.58, 95% CI: 0.44-0.77) or obese (OR=0.48, 95% CI: 0.57-0.62) compared to normal or underweight] and tests after November 20, 2020 [(OR=1.35;95% CI: 1.13-1.71) compared to prior dates]. Conclusion Asymptomatic rates in Olmsted County prior to vaccine rollout ranged from 6-25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.

Association of outpatient use of renin-angiotensin-aldosterone system blockers on outcomes of acute respiratory illness during the COVID-19 pandemic: a cohort study.

OBJECTIVES: Evaluate the associations between patients taking ACE inhibitors and angiotensin receptor blockers (ARBs) and their clinical outcomes after an acute viral respiratory illness (AVRI) due to COVID-19. DESIGN: Retrospective cohort. SETTING: The USA; 2017-2018 influenza season, 2018-2019 influenza season, and 2019-2020 influenza/COVID-19 season. PARTICIPANTS: People with hypertension (HTN) taking an ACEi, ARB or other HTN medications, and experiencing AVRI. MAIN OUTCOME MEASURES: Change in hospital admission, intensive care unit (ICU) or coronary care unit (CCU), acute respiratory distress (ARD), ARD syndrome (ARDS) and all-cause mortality, comparing COVID-19 to pre-COVID-19 influenza seasons. RESULTS: The cohort included 1 059 474 episodes of AVRI (653 797 filled an ACEi or ARB, and 405 677 other HTN medications). 58.6% were women and 72.9% with age ≥65. The ACEi/ARB cohort saw a larger increase in risk in the COVID-19 influenza season than the other HTN medication cohort for four out of five outcomes, with an additional 1.5 percentage point (pp) increase in risk of an inpatient stay (95% CI 1.2 to 1.9 pp) and of ICU/CCU use (95% CI 0.3 to 2.7 pp) as well as a 0.7 pp (0.1 to 1.2 pp) additional increase in risk of ARD and 0.9 pp (0.4 to 1.3 pp) additional increase in risk of ARDS. There was no statistically significant difference in the absolute risk of death (-0.2 pp, 95% CI -0.4 to 0.1 pp). However, the relative risk of death in 2019/2020 versus 2017/2018 for the ACEi/ARB group was larger (1.40 (1.36 to 1.44)) than for the other HTN medication cohort (1.24 (1.21 to 1.28)). CONCLUSIONS: People with AVRI using ACEi/ARBs for HTN had a greater increase in poor outcomes during the COVID-19 pandemic than those using other medications to treat HTN. The small absolute magnitude of the differences likely does not support changes in clinical practice.

Factors Associated With Severe COVID-19 Infection Among Persons of Different Ages Living in a Defined Midwestern US Population.

OBJECTIVE: To identify risk factors associated with severe COVID-19 infection in a defined Midwestern US population overall and within different age groups. PATIENTS AND METHODS: We used the Rochester Epidemiology Project research infrastructure to identify persons residing in a defined 27-county Midwestern region who had positive results on polymerase chain reaction tests for COVID-19 between March 1, 2020, and September 30, 2020 (N=9928). Age, sex, race, ethnicity, body mass index, smoking status, and 44 chronic disease categories were considered as possible risk factors for severe infection. Severe infection was defined as hospitalization or death caused by COVID-19. Associations between risk factors and severe infection were estimated using Cox proportional hazard models overall and within 3 age groups (0 to 44, 45 to 64, and 65+ years). RESULTS: Overall, 474 (4.8%) persons developed severe COVID-19 infection. Older age, male sex, non-White race, Hispanic ethnicity, obesity, and a higher number of chronic conditions were associated with increased risk of severe infection. After adjustment, 36 chronic disease categories were significantly associated with severe infection. The risk of severe infection varied significantly across age groups. In particular, persons 0 to 44 years of age with cancer, chronic neurologic disorders, hematologic disorders, ischemic heart disease, and other endocrine disorders had a greater than 3-fold increased risk of severe infection compared with persons of the same age without those conditions. Associations were attenuated in older age groups. CONCLUSION: Older persons are more likely to experience severe infections; however, severe cases occur in younger persons as well. Our data provide insight regarding younger persons at especially high risk of severe COVID-19 infection.

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Association of outpatient ACE inhibitors and angiotensin receptor blockers and outcomes of acute respiratory illness: a retrospective cohort study.

OBJECTIVES: Evaluate associations between ACE inhibitors (ACEis) and angiotensin receptor blockers (ARBs) and clinical outcomes in acute viral respiratory illness (AVRI). DESIGN: Retrospective cohort analysis of claims data. SETTING: The USA; 2018-2019 influenza season. PARTICIPANTS: Main cohort: people with hypertension (HTN) taking an ACEi, ARB or other HTN medications, and experiencing AVRI. Falsification cohort: parallel cohort receiving elective knee or hip replacement. MAIN OUTCOME MEASURES: Main cohort: hospital admission, intensive care unit, acute respiratory distress (ARD), ARD syndrome and all-cause mortality. Falsification cohort: complications after surgery and all-cause mortality. RESULTS: The main cohort included 236 843 episodes of AVRI contributed by 202 629 unique individuals. Most episodes were in women (58.9%), 81.4% in people with Medicare Advantage and 40.3% in people aged 75+ years. Odds of mortality were lower in the ACEi (0.78 (0.74 to 0.83)) and ARB (0.64 (0.61 to 0.68)) cohorts compared with other HTN medications. On all other outcomes, people taking ARBs (but not ACEis) had a >10% reduction in odds of inpatient stays compared with other HTN medications.In the falsification analysis (N=103 353), both ACEis (0.89 (0.80 to 0.98)) and ARBs (0.82 (0.74 to 0.91)) were associated with decreased odds of complications compared with other HTN medications; ARBs (0.64 (0.47 to 0.87)) but not ACEis (0.79 (0.60 to 1.05)) were associated with lower odds of death compared with other HTN medications. CONCLUSIONS: Outpatient use of ARBs was associated with better outcomes with AVRI compared with other medications for HTN. ACEis were associated with reduced risk of death, but with minimal or no reduction in risk of other complications. A falsification analysis conducted to provide context on the possible causal implications of these findings did not provide a clear answer. Further analysis using observational data will benefit from additional approaches to assess causal relationships between these drugs and outcomes in AVRI.

Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic.

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.

Tissue-resident CD8+ T cells drive age-associated chronic lung sequelae after viral pneumonia.

Lower respiratory viral infections, such as influenza virus and severe acute respiratory syndrome coronavirus 2 infections, often cause severe viral pneumonia in aged individuals. Here, we report that influenza viral pneumonia leads to chronic nonresolving lung pathology and exacerbated accumulation of CD8+ tissue-resident memory T cells (TRM) in the respiratory tract of aged hosts. TRM cell accumulation relies on elevated TGF-ß present in aged tissues. Further, we show that TRM cells isolated from aged lungs lack a subpopulation characterized by expression of molecules involved in TCR signaling and effector function. Consequently, TRM cells from aged lungs were insufficient to provide heterologous protective immunity. The depletion of CD8+ TRM cells dampens persistent chronic lung inflammation and ameliorates tissue fibrosis in aged, but not young, animals. Collectively, our data demonstrate that age-associated TRM cell malfunction supports chronic lung inflammatory and fibrotic sequelae after viral pneumonia.